main| new issue| archive| editorial board| for the authors| publishing house|
Ðóññêèé
Main page
New issue
Archive of articles
Editorial board
For the authors
Publishing house

 

 


ABSTRACTS OF ARTICLES OF THE JOURNAL "INFORMATION TECHNOLOGIES".
No. 6. Vol. 30. 2024

DOI: 10.17587/it.30.291-299

S. N. Ushakov, Postgraduate Student, A. O. Saveliev, Ph.D. Tech. Sciences, Associate Professor,
National Research Tomsk Polytechnic University, Tomsk, Russian Federation

Comparative Review of Tasks, Approaches and Tools for Automated Knowledge Extraction from the Texts of Scientific Publications

The purpose of this work is to review the existing technologies for automated knowledge extraction from scientific publications. The main tasks include an analysis of existing methods for automated knowledge extraction, as well as an overview of various software tools used to solve this problem. The article presents a description of the main approaches to automated knowledge extraction, such as machine learning, natural language processing and the development of various methodologies for building knowledge graphs. An analysis of existing sources showed that the main problems associated with automated knowledge extraction are the need to create a large amount of labeled data, the processing of complex structured data, and the need to develop new algorithms for working with such data.
Keywords: geometric coverage, nesting, multiply connected orthogonal polygon, matrix decomposition method, restricted decomposition method, semantic parsing

P. 291-299

References

  1. Kamran K., Brown E. D., Mojtaba H., Kiana J. M., Gerber M. S., Barnes L. E. Hdltex: Hierarchical deep learning for text classification, 2017 16th IEEE international conference on machine learning and applications (ICMLA), IEEE, 2019, pp. 364— 371.
  2. Yongjun Z., Erjia Y. Searching bibliographic data using graphs: A visual graph query interface, Journal of Informetrics, 2016, vol. 10, no. 4, pp. 1092— 1107.
  3. Chengbin W., Xiaogang M., Jianguo C., Jingwen C nformation extraction and knowledge graph construction from geoscience literature, Computers geosciences, 2019, vol. 112, pp. 112—120.
  4. Waleed A., Dirk G., Chandra B., Iz B., Miles C., Doug D., Dunkelberger J., Ahmed E., Feldman S., Vu H. et al. Construction of the literature graph in semantic scholar, arXiv preprint arXiv:1805.02262. 2018.
  5. Gnana V. N., Rohit S., Sandhya H. Information retrieval and processing system for news articles in English, 2019 9th International Conference on Advances in Computing and Communication (ICACC), IEEE, 2019, pp. 79—85.
  6. Baladevi C., Sandhya H. Semantic representation of documents based on matrix decomposition, 2018 International Conference on Data Science and Engineering (ICDSE), IEEE, 2019, pp. 1—6.
  7. Venkataraman D., Haritha K. Knowledge representation of university examination system ontology for semantic web, 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, 2018, pp. 1—4.
  8. Miriyala K.,Sajeev G. P. Building semantic based recommender system using knowledge graph embedding, 2021 Sixth International Conference on Image Information Processing (ICIIP), IEEE, 2021, vol. 6, pp. 25—29.
  9. Mauro D. L., Julio C. R. Scikgraph: A knowledge graph approach to structure a scientific field, Journal of Informetrics, 2021, vol. 15, no. 1, pp. 101109.
  10. Schmidhuber J. Deep learning in neural networks: An overview [J], Neural Networks, 2019, vol. 61, pp. 85—117.
  11. Hong-Jie D. Family member information extraction via neural sequence labeling models with different tag schemes [J], BMC Medical Informatics and Decision Making, 2019, vol. 19, no. 10.
  12. Matsuo Y., Ishizuka M. Keyword Extraction from a Single Document Using Word Cooccurrence Statistical Information, International Journal on Artificial Intelligence Tools, 2019, vol. 13, no. 1, pp. 157—169.
  13. Suchanek F. M., Kasneci G., Weikum G. A Core of Semantic Knowledge Unifying WordNet and Wikipedia, Proceedings of the 16th International Conference on World Wide Web (Banff, Alberta, Canada, May 8-12, 2007). "WWW '07. N. Y., ACM Press, 2007, pp. 697—706.
  14. Fomichov V. A. Semantic Mapping of Definitions for Constructing Ontologies of Business Processes, 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), May 23—27, 2022, Opatija, Croatia. Proceedings. Rijeka: Croatian Society for Information, Communication and Electronic Technology — MIPRO, 2022, pp. 1258—1263.
  15. Fomichev V. A. Mathematical foundations for representing the content of messages of computer intelligent agents, Moscow, TEIS, 2007, 176 p., available at: https://publications.hse.ru/books/81055472.
  16. Fomichov V. A. Semantics-Oriented Natural Language Processing: Mathematical Models and Algorithms, New York, Dordrecht, Heidelberg, London, Springer, 2010, 352 p.
  17. Fomichov V. A. SK-languages as a Powerful and Flexible Semantic Formalism for the Systems of Cross-Lingual Intelligent Information Access. Informatica, An Intern. Journal of Computing and Informatics (Slovenia), 2017, vol. 41, pp. 221—232.
  18. Musaev A. A., Grigoriev D. A. Extracting knowledge from text messages: overview and state-of-the-art, Computer Research and Modeling, 2021, vol. 13, no. 6, pp. 1291—1315.
  19. Pimeshkov V. K., Dikovickij V. V., Shishaev M. G. Extraction of relation from natural language texts using statistical and linguistic methods, Trudy Kol'skogo nauchnogo centra RAN, 2020, vol. 11, no. 8 (11), pp. 188—192 (In Russian).
  20. Bobamuradov O. D. Stages of knowledge extraction from electronic information resources, Technical Sciences. Eurasian Union of Scientists (ESU), 2019, vol. 10, no. 19, pp. 130—133.
  21. Davis F. D., Yi M. Y. Improving Computer Skill Training: Behavior Modeling, Symbolic Mental Rehearsal, and the Role of Knowledge Structures, Journal of Applied Psychology, 2019, vol. 89, no. 3.
  22. Goldsmith T., Kraiger K. Structural Knowledge Assessment and Training Evaluation, Improving Training Effectiveness in Work Organizations, 2019, pp. 73—96.
  23. Goldsmith T., Davenport D. M. Assessing Structural Similarity of Graphs, Pathfinder Associative Networks: Studies in Knowledge Organization, 2019, pp. 75—87.
  24. Marshall C. C. Toward an ecology of hypertext annotation, The ninth ACM conference on hypertext and hypermedia: links, objects, time and space—structure in hypermedia systems: links, objects, time and space-structure in hypermedia systems, 2019, pp. 40—49.
  25. Bosselut A., Rashkin H., Sap M., Malaviya C., Celikyilmaz A., Choi Y. COMET: Commonsense transformers for auto­matic knowledge graph construction, Proceedings of the 57th Annual Meeting of the Associationfor Computational Linguistics, Association for Computational Linguistics, Florence, Italy, 2019, pp. 4762—4779.
  26. Ji S., Pan S., Cambria E., Marttinen P., Yu P. S. A survey on knowledgegraphs: Representation, acquisition, and applications, IEEE Transactions on Neural Networks and Learning Systems, 2022, vol. 33, pp. 494—514.
  27. Gao T., Han X., Bai Y., Qiu K., Xie Z., Lin Y., Liu Z., Li P., Sun M., Zhou J. Manual evaluation matters: Reviewing test protocols of dis-tantly supervised relation extraction,in: Findings of the Associationfor Computational Linguistics, ACL-IJCNLP 2021, Association for Computational Linguistics, Online, 2021, pp. 1306—1318.
  28. Bollacker K., Evans C., Paritosh P., Sturge T., Taylor J. Freebase: A collaboratively created graph database for structuring human knowledge, Pro-ceedings of the 2018 ACM SIGMOD International Conference on Management of Data, SIGMOD '08, Association for Computing Machinery, NewYork, NY, USA, 2019, pp. 1247—1250.

 

To the contents